2 citations · 2 across the 19 of their papers we have counts for
19 papers
SemTrace: Source-Grounded Semantic Signatures for Tracing LLM Exposure to Protected Documents
Junyan Zhang, Yudong Zeng, Yongwei Huang +3
Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot control or inspect the model…
EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation
Sihang Jia, Shuliang Liu, Songbo Yang +1
Large vision-language models (LVLMs) frequently generate content unsupported by visual inputs. Preliminary experiments show that visual evidence is primarily incorporated into answ…
Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation
Sihang Jia, Shuliang Liu, Songbo Yang +3
Multimodal Large Language Models frequently suffer from inference hallucinations, partially stemming from language priors dominating visual evidence. Existing training-free mitigat…
Correct Prediction, Wrong Steps? Consensus Reasoning Knowledge Graph for Robust Chain-of-Thought Synthesis
Zipeng Ling, Shuliang Liu, Seonil Son +4
Large language models (LLMs) have become increasingly used for various tasks, often coupled with Chain-of-Thought (CoT) prompting to boost accuracy. Recent work has shown that high…
A Visual Semantic Adaptive Watermark grounded by Prefix-Tuning for Large Vision-Language Model
Qi Zheng, Shuliang Liu, Yu Huang +8
Watermarking has emerged as a pivotal solution for content traceability and intellectual property protection in Large Vision-Language Models (LVLMs). However, vision-agnostic water…
CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models
Yu Zhang, Shuliang Liu, Xu Yang +1
Watermarking algorithms for Large Language Models (LLMs) effectively identify machine-generated content by embedding and detecting hidden statistical features in text. However, suc…
MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning
Sicheng Tao, Jungang Li, Yibo Yan +8
Video reasoning has emerged as a critical capability for multimodal large language models (MLLMs), requiring models to move beyond static perception toward coherent understanding o…
PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints
Jiahao Huo, Shuliang Liu, Bin Wang +5
Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing attacks by treating the sentence as…
Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms
Zipeng Ling, Shuliang Liu, Yuehao Tang +8
Large Language Models (LLMs) annotated datasets are widely used nowadays, however, large-scale annotations often show biases in low-quality datasets. For example, Multiple-Choice Q…
A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
Shuliang Liu, Hongyi Liu, Aiwei Liu +7
The widespread deployment of large language models (LLMs) across critical domains has amplified the societal risks posed by algorithmically generated misinformation. Unlike traditi…
LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty
Zipeng Ling, Shuliang Liu, Yuehao Tang +8
Large Language Models (LLMs) are increasingly trained to abstain from answering questions they are unsure about. However, this ability is often misapplied: in real-world applicatio…
VLA-Mark: A cross modal watermark for large vision-language alignment model
Shuliang Liu, Qi Zheng, Jesse Jiaxi Xu +8
Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual…
Video Signature: Implicit Watermarking for Video Diffusion Models
Yu Huang, Junhao Chen, Shuliang Liu +6
The rapid development of Artificial Intelligence Generated Content (AIGC) has led to significant progress in video generation, but also raises serious concerns about intellectual p…
Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities
Junyan Zhang, Yubo Gao, Yibo Yan +8
The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improv…
Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?
Junyan Zhang, Yiming Huang, Shuliang Liu +2
The rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. This study challenges the prevailing "LLM-centric" tren…
RePPL: Recalibrating Perplexity by Uncertainty in Semantic Propagation and Language Generation for Explainable QA Hallucination Detection
Yiming Huang, Junyan Zhang, Zihao Wang +5
Large Language Models (LLMs) have become powerful, but hallucinations remain a vital obstacle to their trustworthy use. Previous works improved the capability of hallucination dete…
CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality
Junyan Zhang, Shuliang Liu, Aiwei Liu +4
Watermarking technology is a method used to trace the usage of content generated by large language models. Sentence-level watermarking aids in preserving the semantic integrity wit…
TreeHop: Efficient Embedding-Level Query Rewriter
Zhonghao Li, Kunpeng Zhang, Jinghuai Ou +2
Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multi…
VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models
Xuming Hu, Hanqian Li, Jungang Li +5
This work introduces \textbf{VideoMark}, a distortion-free robust watermarking framework for video diffusion models. As diffusion models excel in generating realistic videos, relia…